Reading Notes: Statistics 101 (Part 3)

Data collection methods in experiment research
1. to manipulate the independent variable using different entities.
== a between-groups, between-subjects, or independent design.
2. to manipulate the independent variable using the same entities.
== this means that giving a group of students positive reinforcement for a few weeks and test their statistical abilities and then begin to give this same group punishment for a few weeks before testing them again, and then finally give them no motivator and test them for a third time.
== a within-subject or repeated-measures design.

Data collection method determines the type of test that is used to analyse the data.

Andy Field:
The reason why some people think that certain statistical tests allow causal inferences is that historically certain tests (e.g., ANOVA, t-tests, etc.) have been used to analyse experimental research, whereas others (e.g., regression, correlation) have been used to analyse correlational research (Cronbach, 1957)...these statistical procedures are, in fact, mathematically identical.

Two sources of variation:
Systematic variation: This variation is due to the experimenter doing something in one condition but not in the other condition.
Unsystematic variation: This variation results from random factors that exist between the experimental conditions (such as natural differences in ability, the time of day, etc.).

In a repeated-measures design, differences between two conditions can be caused by only two things:
(1) the manipulation that was carried out on the participants, or
(2) any other factor that might affect the way in which an entity performs from one time to the next.
== The latter factor is likely to be fairly minor compared to the influence of the experimental manipulation.

In an independent design, differences between the two conditions can also be caused by one of two things:
(1) the manipulation that was carried out on the participants, or
(2) differences between the characteristics of the entities allocated to each of the groups.
== The latter factor in this instance is likely to create considerable random variation both within each condition and between them.

When we look at the effect of our experimental manipulation, it is always against a background of ‘noise’ caused by random, uncontrollable differences between our conditions.

In a repeated-measures design this ‘noise’ is kept to a minimum and so the effect of the experiment is more likely to show up.

This means that, other things being equal, repeated-measures designs have more power to detect effects than independent designs.

The two most important sources of systematic variation in repeated-measures design are:
Practice effects: Participants may perform differently in the second condition because of familiarity with the experimental situation and/or the measures being used.

Boredom effects: Participants may perform differently in the second condition because they are tired or bored from having completed the first condition.

Randomization: the process of doing things in an unsystematic or random way. In the context of experimental research the word usually applies to the random assignment of participants to different treatment conditions.